Trip Scheduling and the Cost of Congestion: Estimates Using Travel Diary Data and Big Data

The first goal of this project is to empirically quantify the economic cost of traffic congestion for car commuters in California using a new methodology developed in this project. The second goal is to develop a practically implementable congestion tolling policy based on our new theoretical framework and empirical findings on trip scheduling patterns.

To estimate the cost of congestion, we develop a new methodology of directly identifying individual commuters’ congestion delays. Specifically, we collect Google Maps data on departure times and travel times for routes that are unique to individuals, and using the information on actual travel routes and trip timing choices reported in the California Household Travel Survey, we then identify each individual’s congestion delay, which then allows us to quantify the aggregate cost of the state. We also develop an economic conceptual framework matching this empirical setting. We also explore the scheduling behavior of individual commuters and explore its implications on congestion costs.

Our new theoretical framework and empirical findings also gives us new insights on the congestion tolling policy. Specifically, the Google travel time profiles constructed at the travel route level, interacted with individuals’ trip timing choices, provide practically implementable congestion toll schedules that are unique to individuals. The responsiveness of commuters to congestion dynamics provide implications on the effectiveness of a hypothetical “time-varying” congestion tolling policy that are designed to induce re-scheduling of trips and thus remove congested travels.

Principal Investigator: 
Jinwon Kim
PI Contact Information:
California State University, Long Beach

Project Number: 



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